In this study, a large multi-institutional group led by investigators from the Broad Institute in Boston report the initial results from genome sequencing of multiple myeloma (MM) cells. Using the newest technologies of massively parallel DNA sequencing, they performed whole-genome sequencing and/or whole-exome sequencing on MM samples from 38 patients and found significant levels of mutation in many genes, including KRAS and NRAS, TP53, CCND1, DIS3, FAM46C, LRRK2, IRF4, PRMD1, BRAF, NF-κB, histone modifying enzymes, and components of the blood coagulation cascade. These studies confirmed previously known mutations and revealed novel mutations, which may yield insights into pathways involved in MM pathogenesis and suggest potential novel therapeutic targets. Ultimately, this study may help to identify hallmark genomic abnormalities in MM and allow for more effective personalized therapies.
Marked genetic heterogeneity has been demonstrated in MM, with important implications for tumor pathogenesis, prognosis, and treatment. For conventional therapy, hyperdiploidy and t(11;14) have defined standard-risk MM with superior outcome, whereas nonhyperdiploidy, t(4:14), del (17p), and del(13q14) have defined high-risk MM with inferior outcome. However, novel therapies such as bortezomib can overcome, at least in part, the adverse outcome conferred by some [t(4:14)] but not other [del (17p] abnormalities; the latter continues to define high-risk disease. Currently, mRNA (microarray), DNA (array comparative genomic hybridization [aCGH] and single nucleotide polymorphism [SNP]), and microRNA (miRNA) profiling studies of clinically annotated samples from uniformly treated patients are being used to characterize molecular pathogenesis of MM, identify novel targets, and develop refined patient stratification and personalized medicine approaches in MM. Microarray profiling has revealed transcriptional changes correlating with evolution from monoclonal gammopathy of undetermined significance (MGUS) to smoldering MM (SMM) to active MM; this has led to transcript-based prognostic MM classification systems and new definitions of high-risk MM. Already genetic and molecularly distinct subgroups of MM have distinct biology and treatment options; for example,
FGFR3 inhibitor therapy may be useful in t(4:14) MM and rituxiban therapy in CD20-positive MM. DNA-based aCGH and SNP array studies have identified copy number alterations (CNAs), which predict for clinical outcome, including increased 1q and 5q as sites for putative MM oncogenes, as well as decreased 12p as a site of putative MM suppressor genes. miRNA profiling studies can distinguish normal plasma cells from MM cell lines: patients whose tumors resemble the former have improved outcome versus patients with tumors resembling cell lines.
In Brief
Previous studies in MM have revealed activating mutations of oncogenes (MYC, RAS, FGFR3), inactivation of various tumor suppressors (p53, p18, RB1), CNAs/ mutations leading to activation of NF-κB pathway, and inactivating mutations and deletions of demethylase genes such as UTX. Importantly, this new MM sequencing study confirmed mutations of KRAS, NRAS, TP53, and NF-κB. It also revealed novel mutated genes involved in protein homeostasis, consistent with MM as the prototype cancer for therapeutic targeting with proteasome inhibition. Identification of novel CCND1 mutations also supports the central role of cyclin D1 in MM. Mutations in IRF4 and PRMD1 are also consistent with prior studies implicating these genes in normal and malignant plasma cell differentiation. Importantly, mutations in histone methylating enzymes confirm the inactivating mutations of UTX and further support efforts to target histone methyltransferases, such as MMSET. Of note and unexpectedly, mutations in BRAF, a gene encoding a serine/threonine kinase known to be involved in pathogenesis of melanoma, were described in 4 percent of cases, which may have immediate clinical application. Current and future studies are characterizing larger numbers of samples to identify less common mutations and define interrelationships of mutations; sequencing RNA for allele-specific expression, differential expression, and more accurate sample clustering; utilizing high-throughput gain/loss-of-function assays to identify driver mutation effects, with focused validation studies of leads; and performing longitudinal studies to assess evolution of changes with acquisition of drug resistance and disease progression.
